Citations

AbstractIntroductionThis study evaluates the feasibility of utilizing a commercial‐grade 3D LiDAR/RGB camera, combined with 3D reconstruction software, to create an accurate and hyper‐realistic digital model of a proton therapy treatment room. This reconstructed model aims to enhance collision avoidance strategies and improve 3D machine model accessibility. Utilization of a 3D LiDAR/RGB camera as a radiotherapy environment modeling has not been reported prior.MethodsTo create a comprehensive 3D model of a half‐gantry proton therapy system, colored point cloud data was captured using the Matterport Pro 3 camera, a hybrid LiDAR and RGB imaging system with high‐resolution capabilities (134.2 MP), a 360° horizontal field of view, and 20 mm accuracy at 10 m. A total of 117 acquisition points, distributed across the treatment room with three camera heights, ensured complete coverage and minimal occlusions. Scanning, completed in under 3 hours, was monitored in real time using the Matterport App. Post‐scan processing involved denoising and converting the point cloud into 3D mesh structures using MeshLab, followed by refined pair‐wise iterative closest point (ICP) alignments. Textures and materials were assigned to reflect real‐world objects, and a ray‐tracing engine simulated realistic lighting in Blender. Animations illustrating the kinematics of the treatment couch and gantry were simulated in Blender for enhanced visualization.ResultsThe scanning process achieved complete data capture without missing information, attributed to strategic oversampling during data acquisition. The mean root mean square error (RMSE) of the ICP registration was 0.008 m. The validation process confirmed that the dimensions of the treatment couch within the digital model closely matched the actual measurements with less than 2 cm deviation, indicating high accuracy. The resulting digital twin provided a photorealistic and immersive representation of the proton therapy treatment room, serving as a valuable digital asset for various applications.ConclusionIt is possible to generate vendor‐independent and highly accurate 3D models of the proton treatment room environment with a commercial grade LiDAR/RGB camera to expand future research opportunities and education endeavors.

Abstract Background Dose‐driven continuous scanning (DDCS) is a type of beam delivery in Hitachi pencil beam scanning (PBS) particle therapy system. Particle beam is irradiated continuously between spots to achieve a higher effective dose rate and a shorter beam delivery time. The beam is only turned off at break spots, the end of a treatment layer and during spill change. The break spot is a unique feature of the DDCS introduced by the treatment planning system (TPS) to interrupt continuous scanning. DDCS separates the dose delivery of a spot into a move dose and stop dose components. The move dose is controlled by the scanning speed and beam current, while the stop dose delivers the remaining monitor units (MUs). Hence, rigorous quality assurance (QA) of the scanning speed and functionality of break spot are required to ensure safe and successful DDCS delivery. Purpose We aim to report for the first time the QA methodology and result for DDCS, focusing on the scanning speed and break spot validation. Methods A rectangular spiral irradiation pattern with different degree of break spots were irradiated on a 2D strip ionization chamber (CROSSmini) detector using 50 time resolution to validate the break spots. Four methods of measuring scanning speeds were proposed. Using a spiral irradiation pattern, methods one and two applied linear regression to the scanning time measured by the CROSSmini for different inter‐spot distances, with measurements taken at sampling rates of 5 and 20 kHz, respectively. Methods three and four measured the scanning time of single inter‐spot distances in the X and Y directions. Method three measured the scanning time with the CROSSmini while method four measured the rise and fall time of the scanning magnet (SCM) current. All measurements were performed with 70.2, 150.2 and 228.7 MeV proton beam energies and three different beam currents of 8, 14 and 20 MU/s. Lastly, the scanning speed tolerance limit was calculated analytically under the requirement of avoiding a beam abort when the stop dose was less than zero. Results The data recorded by the CROSSmini under the spiral irradiation patterns with break spots was reconstructed into a 2D dose, and the break spots’ operations were visually validated for all the energies and beam currents. There were no statistically significant difference in the measured X and Y scanning speeds across the four methods. However, the last method involving the SCM current yielded the largest type A uncertainty of up to 25% () due to the noisy signal. The first method with the linear regression and 5 kHz acquisition rate was preferred due to a smaller type A uncertainty of up to 2.0% and a lower false negative rate in detecting the rising and falling edges during scanning. The calculated tolerance limit based on our current DDCS setting was found to be 0.50 times of the expected scanning speed. Conclusion We have introduced QA protocols for DDCS, designed to ensure complete and safe beam delivery without interruptions caused by move doses exceeding planned spot doses.

Abstract Background Cone‐beam computed tomography (CBCT) is essential for image‐guided particle therapy (IGPT), providing daily patient positioning, anatomical monitoring, and treatment verification. However, conventional single‐detector CBCT suffers from poor soft‐tissue contrast, long acquisition times, and motion artifacts, reducing its effectiveness for adaptive radiotherapy workflows. Addressing these challenges requires an advanced CBCT reconstruction approach capable of enhancing image quality while reducing scan time and radiation exposure. Purpose This study introduces a Dual Detector Combined Scanning (DDCS) CBCT reconstruction algorithm to overcome conventional CBCT limitations. By integrating a dual‐source, orthogonal imaging setup, DDCS significantly reduces scanning time and imaging artifacts. A modified Parker‐weighting algorithm further improves image reconstruction accuracy, ensuring high‐fidelity visualization. The goal is to enhance adaptive radiotherapy by improving real‐time patient positioning, dose verification, and motion management. Methods A dual‐detector CBCT system was designed to acquire orthogonal projections simultaneously, reducing scan time and improving data completeness. A modified Parker‐weighting algorithm corrected angular overlap distortions, ensuring better reconstruction accuracy. The system was evaluated using numerical and physical phantom studies to assess spatial resolution, contrast‐to‐noise ratio (CNR), and artifact reduction. Additionally, a dynamic phantom simulated respiratory motion, validating motion robustness for adaptive radiotherapy. Performance was compared against single‐detector CBCT, focusing on image fidelity, noise suppression, and computational efficiency. Results DDCS‐CBCT demonstrated higher CNR, reduced motion artifacts, and improved spatial resolution, leading to more accurate anatomical visualization. The system's efficiency enables faster, more reliable patient setup in IGPT while maintaining a lower imaging dose. Conclusion The proposed DDCS‐CBCT approach significantly improves imaging accuracy, reduces scan time, and enhances real‐time volumetric guidance in IGRT/IGPT. These findings support the clinical feasibility of the DDCS system and its integration into online adaptive radiotherapy workflows.

Abstract Background Proton therapy offers precise tumor targeting while minimizing damage to surrounding healthy tissue, making it especially valuable for treating tumors near critical organs and in pediatric patients. However, its success depends on accurate beam delivery, requiring rigorous quality assurance (QA) to maintain treatment precision and effectiveness. Purpose This study aims to standardize the measurement of key aspects of proton therapy delivery, such as gantry and couch isocentricity, spot position accuracy, and spot size consistency. The goal is to enhance the delivery accuracy of proton therapy across various clinical settings, improving patient outcomes. Methods The QA framework uses the Octopoint phantom, Gafchromic film, and spot position monitor (SPM) log data to evaluate proton beam isocentricity, spot position, and spot size. The Octopoint phantom, made from acrylic, was used with Gafchromic films at various gantry and couch angles to measure isocentricity. MATLAB tools were used to analyze spot positions, and SPM logs provided verification. Sensitivity tests were conducted to assess the system's response to intentional shifts and errors, ensuring alignment with clinical QA standards. Results The Octopoint phantom's stepwise two‐dimensional (2D) fitting process, validated against film‐measured dose profiles, accurately identified beam and ball bearing (BB) centroid locations. Isocentricity tests conducted over 8 months across four gantries demonstrated consistent beam‐to‐BB radial offsets. The phantom showed excellent repeatability, with a maximum standard deviation of 0.1 mm across various couch‐gantry combinations. Sensitivity testing across all axes revealed a strong alignment between intended shifts and measured values. Over the course of a year, film measurements tracked spot position and size consistency, with deviations remaining within acceptable clinical limits. Comparisons with SPM data further confirmed the system's reliability in maintaining beam accuracy across different gantry angles and energy levels. Conclusions This study presents a reliable QA framework for ensuring precision in proton therapy delivery. By combining the Octopoint phantom, Gafchromic film analysis, and SPM log file evaluation, we effectively measured isocentricity, spot position accuracy, and spot size stability. The framework demonstrated adaptability across various clinical QA tasks, enhancing the accuracy and safety of proton therapy treatments.

Abstract Introduction: To evaluate a proof-of-concept three-dimensional surface reconstruction technique using a hybrid LiDAR and RGB sensor system with an open-source, GPU-accelerated pipeline. The goal is to generate photorealistic digital twins of phantom surfaces for integration into radiotherapy collision avoidance workflows. Methods: A portable Intel RealSense sensor was used to acquire synchronized depth and color images. Sensor performance, including depth accuracy, fill rate, and planar root mean square error, was evaluated to determine optimal scan range. A reconstruction pipeline was implemented using the Open3D library with a voxel-based framework, signed distance function integration, ray casting, and color and depth-based simultaneous localization and mapping for pose tracking. Surface meshes were generated using the Marching Cubes algorithm. Validation involved scanning rectangular box phantoms and an anthropomorphic Rando phantom in a single circular motion. Reconstructed models were registered to CT-derived meshes using manual point picking and iterative closest point alignment. Accuracy was assessed using cloud-to-mesh distance metrics and compared to Poisson surface reconstruction. Results: Highest accuracy was observed within the 0.3 to 2.0 meter range. Volume differences for box models were within five millimeters. The Rando phantom showed a registration error of 1.8 millimeters and full overlap with the CT reference. Mean signed distance was minus 0.32 millimeters with a standard deviation of 3.85 millimeters. Conclusions: This technique enables accurate, realistic surface modeling using low-cost, open-source tools and supports future integration into radiotherapy digital twin systems.

Microdosimetry plays a critical role in particle therapy by quantifying energy deposition within microscopic domains to assess biological effects. This study evaluates the influence of different microdosimetric functions (MFs) and domain geometries (DGs) on relative biological effectiveness (RBE) predictions in carbon ion radiotherapy. Specifically, we compare the analytical microdosimetric function (AMF), calculated for spherical domains and implemented in PHITS, with the Kiefer–Chatterjee (KC) track structure model, which is conventionally applied to cylindrical geometries. To enable a direct comparison, we also introduce a novel implementation of the KC model for spherical domains. Using both models, specific energy distributions were calculated across a range of domain sizes and geometries. These distributions were input into the modified microdosimetric kinetic model (mMKM) to calculate RBE for the HSG cell line and compared against published in vitro data. The results show that both microdosimetric function and domain geometry significantly affect microdosimetric spectra and the resulting RBE, with deviations exceeding 10% when fixed mMKM parameters are used. Parameter optimization within the mMKM enables alignment across models. Our findings emphasize that microdosimetric function and domain geometry selection must be explicitly accounted for in microdosimetry-based RBE modeling, and that model parameters must be tuned accordingly to ensure consistent and biologically accurate predictions.

Abstract Background Projectile and target fragmentation are nuclear phenomena that can influence the computation of the linear energy transfer (LET) and the relative biological effectiveness (RBE) in external radiotherapy with accelerated ions. Correlations between these two quantities are routinely established during radiobiological experiments to interpret the results and to develop and calibrate RBE models. Purpose This study systematically evaluates the impact of secondary fragments and irradiation scenarios on the dose‐averaged LET, the RBE, and their correlation in the case of exposures to clinically relevant ion beams. Methods 57 600 microdosimetric lineal energy spectra and corresponding LET distributions were simulated with the Monte Carlo code PHITS across different scenarios, including track segment calculations, pristine, and spread‐out Bragg peaks of 1 H, 4 He, 12 C, 16 O, and 20 Ne ions within water phantoms. The LET distributions were analyzed to calculate the dose‐average LET, both including or excluding the contribution of secondary ions of an element different from the primary beam. Similarly, the lineal energy distributions were processed in conjunction with the Mayo Clinic Florida microdosimetric kinetic model to compute the RBE for two theoretical cell lines ( α / β = 2 and 10 Gy in the case of 6 MV x‐rays). Correlations between the RBE and dose‐averaged LET were established by analyzing the simulation results within water phantoms and then compared to the corresponding trends from the track segment calculations. Results Excluding secondary fragments had a pronounced impact on the dose‐averaged LET and the RBE, particularly in the entrance regions of proton beams and close to the distal edge of heavier ions. The correlations between the RBE and the dose‐averaged LET were not universal, but highly dependent on the irradiation scenario. For proton beams only, the dose‐averaged LET of hydrogen ions served as a practical first‐order descriptor of the RBE. Track segment simulations, commonly used for calibrating and benchmarking RBE models, provided a reasonable approximation for low‐energy beams but failed to fully capture the complexity of polyenergetic radiation fields. Conclusions Secondary fragments can substantially affect the dose‐averaged LET and the RBE, even in proton beams. The dose‐averaged LET, including or not the contributions from secondary fragments, was generally unable to adequately capture RBE variations across different scenarios. A more comprehensive approach, considering microdosimetric distributions, is necessary to accurately describe RBE variations in ion therapy.

Ion radiotherapy requires accurate relative biological effectiveness (RBE) calculations to account for the markedly different biological effects of ions compared to photons. Microdosimetric RBE models rely on descriptions of the energy deposition at the microscopic scale, either through radial dose distributions (RDDs) or microdosimetric probability density distributions. While RDD approaches focus on the theoretical description of the energy deposition around the ion track, microdosimetric distributions offer the advantage of being experimentally measurable, which is crucial for quality assurance programs. As the results of microdosimetric RBE models depend on whether RDD or microdosimetric distributions are used, the model parameters are not interchangeable between these approaches. This study presents and validates a method to reproduce the published reference biological and clinical dose calculations at NIRS-QST for only carbon ion radiotherapy by using the modified microdosimetric kinetic model (MKM) alongside microdosimetric distributions instead of the reference RDD approach. To achieve this, Monte Carlo simulations were performed to estimate the variation of the radiation quality within and outside the field of pristine and spread-out Bragg peaks. By appropriately optimizing the modified MKM parameters for microdosimetric distributions assessed within water spheres, we successfully reproduced the results of calculations using the reference NIRS-QST RDD, generally within 2%.

Abstract Background All Hitachi proton pencil beam scanning facilities currently use discrete spot scanning (DSS). Mayo Clinic Florida (MCF) is installing a Hitachi particle therapy system with advanced technologies, including fast scan speeds, high beam intensity, rapid beam off control (RBOC), a skip spot function, and proton pencil beam scanning using dose driven continuous scanning (DDCS). A potential concern of RBOC is the generation of a shoulder at the end of the normal spot delivery due to a flap spot (FS) with a flap dose (FD), which has been investigated for carbon synchrotron but not for proton delivery. While investigated, for instance, for Hitachi's installation at MCF, this methodology could be applicable for all future high intensity proton deliveries. Purpose No Hitachi proton facility currently uses the proposed RBOC. This study aimed to understand the dosimetric impact of proton FD at MCF by simulating the FS with a Hitachi proton machine in research mode, reflecting the higher proton intensities expected with RBOC at MCF. Method Experiments were conducted to simulate MCF RBOC at Kyoto Prefecture University of Medicine (KPUM) in research mode, reducing delay time (Td) from 1.5 ms to 0.1 ms. 5,000 contiguous spots were delivered on the central axis for proton energies of 70.2, 142.5, and 220.0 MeV; at normal, high dose rate (HDR), and ultra‐high dose rate (uHDR) intensities; and at vertical and horizontal gantry angles for different Td. Measurements were taken using a fast oscilloscope and the nozzle's spot position monitor (SPM) and dose monitor (DM). A model was developed to predict FD dependence on beam intensity and assess the dosimetric impact for prostate and brain treatment plans. Two simulation types were planned: a flap DSS plan with FS at every spot and a flap DDCS plan with FS only at the end of each layer. Result FD was observed for RBOC with Td = 0.1 ms, showing no gantry angle dependence. FD increased with higher delayed dose rate (DDR), that is, beam intensity. The planning study showed dose volume histogram deterioration with increased FD compared to the clinical plan, but it was only significant for uHDR intensities. Deterioration was marginal in flap DSS plans for the HDR intensities planned at MCF, and flap DDCS plans were even less sensitive than flap DSS plans. Conclusion MCF is installing proton DDCS with higher beam intensities, a skip spot function, and fast beam‐off control. The resulting FD had an insignificant impact on dose distribution for two patient plans with both DSS and DDCS at the anticipated MCF intensities. However, significant dependence was observed in the case of uHDR. A method to measure the position and dose of the FS during commissioning is described in addition to recommendations for regular QA and log‐based proton patient‐specific quality assurance.

Adaptive radiotherapy (ART) workflows are increasingly adopted to achieve dose escalation and tissue sparing under dynamic anatomical conditions. However, recontouring and time constraints hinder the implementation of real-time ART workflows. Various auto-segmentation methods, including deformable image registration, atlas-based segmentation, and deep learning-based segmentation (DLS), have been developed to address these challenges. Despite the potential of DLS methods, clinical implementation remains difficult due to the need for large, high-quality datasets to ensure model generalizability. This study introduces an InterVision framework for segmentation. The InterVision framework can interpolate or create intermediate visuals between existing images to generate specific patient characteristics. The InterVision model is trained in two steps: (1) generating a general model using the dataset, and (2) tuning the general model using the dataset generated from the InterVision framework. The InterVision framework generates intermediate images between existing patient image slides using deformable vectors, effectively capturing unique patient characteristics. By creating a more comprehensive dataset that reflects these individual characteristics, the InterVision model demonstrates the ability to produce more accurate contours compared to general models. Models are evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) for 18 structures in 20 test patients. As a result, the Dice score was 0.81 ± 0.05 for the general model, 0.82 ± 0.04 for the general fine-tuning model, and 0.85 ± 0.03 for the InterVision model. The Hausdorff distance was 3.06 ± 1.13 for the general model, 2.81 ± 0.77 for the general fine-tuning model, and 2.52 ± 0.50 for the InterVision model. The InterVision model showed the best performance compared to the general model. The InterVision framework presents a versatile approach adaptable to various tasks where prior information is accessible, such as in ART settings. This capability is particularly valuable for accurately predicting complex organs and targets that pose challenges for traditional deep learning algorithms.